Abstract Data

Data is everywhere today. Yet “data” rarely refers to the direct and unambiguous representation of facts in numbers (for example, “How many screws of a given type are in stock?”); instead, it usually refers to abstract concepts. Even terms that are supposedly unambiguous turn out to be highly abstracted concepts (the seemingly unambiguous question “How many deadlines were met on time?” is, for example, constructed based on the definitions of “What margin of error is still considered on time?” and “What exactly must be completed?”).

That shifts the epistemological challenge: The difficulties of interpretation then lie less in the mathematical processing of the data than in understanding the abstractions underlying them. Only those who have thoroughly grasped the concepts can draw conclusions from the data relating to actual facts at all. Or, to put it even more extremely: Only those who know the underlying, abstracted reality in meticulous detail—and have long already interpreted it conclusively—can evaluate the plausibility of the presumed insights derived from the data.

This leads to an absurd situation: Only those who do not actually need the data can make use of it.